Update app.py

app.py

@@ -60,38 +60,19 @@ def predict(query):
     return real
 
 def findRealProb(text):
-    data = request.json.get('text')
-    if data is None or len(data) == 0:
-        return jsonify({'error': 'No query provided'}), 400
-    if len(data) > 9400:
-        return jsonify({'error': 'Cannot analyze more than 9400 characters!'}), 400
-    if count_words(data) > 1500:
-        return jsonify({'error': 'Cannot analyze more than 1500 words'}), 400
-    
-    # return {"Real": predict(data)}
-    chunksOfText = (chunks_of_900(data))
+    chunksOfText = (chunks_of_900(text))
     results = []
     for chunk in chunksOfText:
-        outputv1 = predict(chunk, model, tokenizer)
-        # outputv2 = predict(chunk, modelv2, tokenizerv2)
-        label = "CG"
-        if(outputv1>=0.5):
-            label = "OR"
-        results.append({"Text":chunk, "Label": label, "Confidence":(outputv1)})    
+        output = predict(chunk)
+        results.append([output, len(chunk)])
+    
     ans = 0
     cnt = 0
-    for result in results:
-        length = len(result["Text"])
-        confidence = result["Confidence"]
+    for prob, length in results:
         cnt += length
-        ans = ans + (confidence)*(length)
+        ans = ans + prob*length
     realProb = ans/cnt
-    label = "AI"
-    if realProb > 0.7:
-        label = "Human"
-    elif realProb > 0.3 and realProb < 0.7:
-        label = "Might be AI"
-    return jsonify({"Real": realProb, "Fake": 1-realProb, "Label": label,  "Chunks": results})
+    return {"Real": realProb, "Fake": 1-realProb}, results
 
 demo = gr.Interface(
         fn=findRealProb,